Different from previous methods that choose a fixed number of best scoring anchors, statistics-based label assignment detectors model the anchor selection procedure as a likelihood estimation for a probability distribution. ATSS [1]} proposes an adaptive training sample selection method that uses the sum of mean and standard deviation as the IoU Threshold. PAA [2]} proposes a probabilistic anchor assignment strategy that adaptively separates a set of anchors into positive and negative samples for a GT box according to the learning status of the model associated with it. These methods have taken a step towards more accurate assignment strategies. Nevertheless, to the best of our knowledge, these still get caught into the IoU-based assignment, which means they still not escape from the inconsistent problem our work raised.
